BERTopic_Environmental
This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
Usage
To use this model, please install BERTopic:
pip install -U bertopic
You can use the model as follows:
from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Environmental")
topic_model.get_topic_info()
Topic overview
- Number of topics: 26
- Number of training documents: 905
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | electric - car - cars - charging - vehicles | 11 | -1_electric_car_cars_charging |
| 0 | battery - batteries - lithium - catl - technology | 213 | 0_battery_batteries_lithium_catl |
| 1 | byd - charging - dolphin - chinese - new | 61 | 1_byd_charging_dolphin_chinese |
| 2 | charging - ev - chargers - ev charging - electric | 58 | 2_charging_ev_chargers_ev charging |
| 3 | zero - government - uk - mandate - electric | 57 | 3_zero_government_uk_mandate |
| 4 | electric - charging - points - france - car | 49 | 4_electric_charging_points_france |
| 5 | battery - lithium - recycling - batteries - supply | 48 | 5_battery_lithium_recycling_batteries |
| 6 | cars - combustion - study - electric - car | 36 | 6_cars_combustion_study_electric |
| 7 | percent - cars - market - sales - vehicles | 33 | 7_percent_cars_market_sales |
| 8 | fires - safety - battery - electric - cars | 29 | 8_fires_safety_battery_electric |
| 9 | charging - electric - sweden - vehicles - circle | 29 | 9_charging_electric_sweden_vehicles |
| 10 | tax - drivers - petrol - ev - rates | 25 | 10_tax_drivers_petrol_ev |
| 11 | kia - car - model - electric - range | 25 | 11_kia_car_model_electric |
| 12 | cent - car - petrol - evs - drivers | 23 | 12_cent_car_petrol_evs |
| 13 | charging - stations - charging stations - charging points - points | 23 | 13_charging_stations_charging stations_charging points |
| 14 | india - ev - green - mobility - electric | 23 | 14_india_ev_green_mobility |
| 15 | indonesia - battery - lg - ev - ev battery | 20 | 15_indonesia_battery_lg_ev |
| 16 | department - flames - police - car - tesla | 20 | 16_department_flames_police_car |
| 17 | transport - ireland - council - ev - climate | 19 | 17_transport_ireland_council_ev |
| 18 | toyota - electric - new - europe - hyundai | 19 | 18_toyota_electric_new_europe |
| 19 | sales - new - electric - cent - car | 17 | 19_sales_new_electric_cent |
| 20 | european - commission - eu - von - der | 15 | 20_european_commission_eu_von |
| 21 | power - blackout - spain - homes - electricity | 14 | 21_power_blackout_spain_homes |
| 22 | nissan - leaf - micra - new - generation | 13 | 22_nissan_leaf_micra_new |
| 23 | ship - coast - vessel - coast guard - guard | 13 | 23_ship_coast_vessel_coast guard |
| 24 | id - volkswagen - vw - every1 - id every1 | 12 | 24_id_volkswagen_vw_every1 |
Training hyperparameters
- calculate_probabilities: False
- language: None
- low_memory: False
- min_topic_size: 10
- n_gram_range: (1, 1)
- nr_topics: None
- seed_topic_list: None
- top_n_words: 10
- verbose: True
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 2.0.2
- HDBSCAN: 0.8.40
- UMAP: 0.5.8
- Pandas: 2.2.2
- Scikit-Learn: 1.6.1
- Sentence-transformers: 4.1.0
- Transformers: 4.53.0
- Numba: 0.60.0
- Plotly: 5.24.1
- Python: 3.11.13
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